Corrector LSTM: built-in training data correction for improved time-series forecasting
Yassine Baghoussi,
Carlos Soares,
João Mendes-Moreira
Abstract:Traditional recurrent neural networks (RNNs) are essential for processing time-series data. However, they function as read-only models, lacking the ability to directly modify the data they learn from. In this study, we introduce the corrector long short-term memory (cLSTM), a Read & Write LSTM architecture that not only learns from the data but also dynamically adjusts it when necessary. The cLSTM model leverages two key components: (a) predicting LSTM’s cell states using Seasonal Autoregressive Integrated… Show more
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